Volume 7, Issue 5 (May 2020), Pages: 56-65

----------------------------------------------

 Original Research Paper

 Title: Data mining approach for digital forensics task with deep learning techniques

 Author(s): Lalbihari Barik *

 Affiliation(s):

 Department of Information Systems, Faculty of Computing and Information Technology in Rabigh, King Abdul Aziz University, Saudi Arabia

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0002-5977-6319

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2020.05.008

 Abstract:

In the past, digital forensic, with its exploration techniques, are a lane to the data recovery as well as the examination of different investigation techniques. It is a line of investigation which includes many stages. In this, the foremost assignment is data collection later than that the outcome amount produced predicted with the dataset. Some authors proposed several supervised machine learning techniques that have not obtained much better results. Therefore, the goal of our study was to perform an investigational work on a forensics dataset task for class-based classification methods like three-layer CNN classifiers, five-layer CNN classifiers, and seven-layer CNN classifiers. The classifiers evaluated with classification performance and accuracy. The experimental plan has been done with fivefold cross-validation with fifty repetitions for deep learning algorithms in order to obtain consistent results. Matching accuracy values for the next to next pixels in the classes are calculated with the class-based predicted labels. There are four classes assigned on CNN, and the four classes are segmented and separated with the same region of interest. Then the same class-based region of interests is segregated, and these four class-based regions are next given to CNN with the clusters. Further, the comparison results are made with the used three algorithms. 

 © 2020 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Digital forensics, Deep learning, Supervised machine learning, CNN classifiers, Class-based regions

 Article History: Received 21 September 2019, Received in revised form 5 February 2020, Accepted 12 February 2020

 Acknowledgment:

The author thanks King Abdulaziz University for this work.

 Compliance with ethical standards

 Conflict of interest: The authors declare that they have no conflict of interest.

 Citation:

 Barik L (2020). Data mining approach for digital forensics task with deep learning techniques. International Journal of Advanced and Applied Sciences, 7(5): 56-65

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 Fig. 7

 Tables

 Table 1

----------------------------------------------

 References (18) 

1. Abraham T (2006). Event sequence mining to develop profiles for computer forensic investigation purposes. In the 2006 Australasian Workshops on Grid Computing and E-Research, Australian Computer Society Inc., Hobart, Australia, 54: 145-153.   [Google Scholar]
2. Agrawal R, Imieliński T, and Swami A (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2): 207-216. https://doi.org/10.1145/170036.170072   [Google Scholar]
3. Beebe NL and Clark JG (2005). A hierarchical, objectives-based framework for the digital investigations process. Digital Investigation, 2(2): 147-167. https://doi.org/10.1016/j.diin.2005.04.002   [Google Scholar]
4. Bhat VH, Rao PG, Abhilash RV, Shenoy PD, Venugopal KR, and Patnaik LM (2010). A data mining approach for data generation and analysis for digital forensic application. International Journal of Engineering and Technology, 2(3): 313-319. https://doi.org/10.7763/IJET.2010.V2.140   [Google Scholar]
5. Caddy B (2001). Forensic examination of glass and paint: Analysis and interpretation. CRC press, Boca Raton, USA. https://doi.org/10.1201/9780203483589   [Google Scholar]
6. Carrier B (2003). Defining digital forensic examination and analysis tools using abstraction layers. International Journal of Digital Evidence, 1(4): 1-12.   [Google Scholar]
7. Casey E (2009). Handbook of digital forensics and investigation. Academic Press, Cambridge, USA. https://doi.org/10.1016/B978-0-12-374267-4.00004-5   [Google Scholar] PMid:20881312
8. Conlan K, Baggili I, and Breitinger F (2016). Anti-forensics: Furthering digital forensic science through a new extended, granular taxonomy. Digital Investigation, 18: S66-S75. https://doi.org/10.1016/j.diin.2016.04.006   [Google Scholar]
9. Fayyad UM, Piatetsky-Shapiro G, and Uthurusamy R (2003). Summary from the KDD-03 panel: Data mining: The next 10 years. ACM SIGKDD Explorations Newsletter, 5(2): 191-196. https://doi.org/10.1145/980972.981004   [Google Scholar]
10. Grajeda C, Breitinger F, and Baggili I (2017). Availability of datasets for digital forensics–And what is missing. Digital Investigation, 22: S94-S105. https://doi.org/10.1016/j.diin.2017.06.004   [Google Scholar]
11. Houck MM (2003). Trace evidence analysis: More cases in forensic microscopy and mute witnesses. Elsevier, Amsterdam, Netherlands.   [Google Scholar]
12. James SH and Nordby JJ (2002). Forensic science: An introduction to scientific and investigative techniques. CRC Press, Boca Raton, USA.   [Google Scholar]
13. Kessler GC (2012). Advancing the science of digital forensics. Computer, 45(12): 25-27. https://doi.org/10.1109/MC.2012.399   [Google Scholar]
14. Mumford CL (2009). Synergy in computational intelligence. In: Mumford CL and Jain LC (Eds.), Computational intelligence: 3-21. Springer, Berlin, Germany. https://doi.org/10.1007/978-3-642-01799-5   [Google Scholar]
15. Pernkopf F (2004). Detection of surface defects on raw steel blocks using Bayesian network classifiers. Pattern Analysis and Applications, 7(3): 333-342. https://doi.org/10.1007/s10044-004-0232-3   [Google Scholar]
16. Popescu AC and Farid H (2004). Statistical tools for digital forensics. In: Fridrich J (Ed.), International workshop on information hiding, Berkeley, USA: 128-147. Springer, Berlin, Germany. https://doi.org/10.1007/978-3-540-30114-1_10   [Google Scholar]
17. Reith M, Carr C, and Gunsch G (2002). An examination of digital forensic models. International Journal of Digital Evidence, 1(3): 1-12.   [Google Scholar]
18. Shahraki AS, Sayyadi H, AMRI MH, and Nikmaram M (2013). Survey: Video forensic tools. Journal of Theoretical and Applied Information Technology, 47(1): 98-107.   [Google Scholar]